In my latest weekend-project I have been using a Variational Autoencoder to build a feature-based face editor. The model is explained in my youtube video. The feature editing is based on modifying the latent distribution of the VAE. After training of the VAE is completed, the latent space is mapped by encoding the training data once more. Latent space vectors of each feature are determined based on the labels of the training data.
Abstract: Recent advancements in machine learning research, i.e., deep learning, introduced methods that excel conventional algorithms as well as humans in several complex tasks, ranging from detection of objects in images and speech recognition to playing difficult strategic games. However, the current methodology of machine learning research and consequently, implementations of the real-world applications of such algorithms, seems to have a recurring HARKing (Hypothesizing After the Results are Known) issue. In this work, we elaborate on the algorithmic, economic and social reasons and consequences of this phenomenon. Furthermore, a potential future trajectory of machine learning research and development from the perspective of accountable, unbiased, ethical and privacy-aware algorithmic decision making is discussed. We would like to emphasize that with this discussion we neither claim to provide an exhaustive argumentation nor blame any specific institution or individual on the raised issues.
Abstract: We show how to teach machines to paint like human painters, who can use a few strokes to create fantastic paintings. By combining the neural renderer and model-based Deep Reinforcement Learning (DRL), our agent can decompose texture-rich images into strokes and make long-term plans. For each stroke, the agent directly determines the position and color of the stroke. Excellent visual effect can be achieved using hundreds of strokes. The training process does not require experience of human painting or stroke tracking data.
Every year trash companies sift through an estimated 68 million tons of recycling, which is the weight equivalent of more than 30 million cars. A key step in the process happens on fast-moving conveyor belts, where workers have to sort items into categories like paper, plastic and glass. Such jobs are dull, dirty, and often unsafe, especially in facilities where workers also have to remove normal trash from the mix. With that in mind, a team led by researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) has developed a robotic system that can detect if an object is paper, metal, or plastic. The team's "RoCycle" system includes a soft Teflon hand that uses tactile sensors on its fingertips to detect an object's size and stiffness.
In recent times, the benefits of chatbots have become more understood, especially in the areas of customer services and sales processes. According to statistics, chatbots are predicted to tackle a massive 85% of customer service interactions by 2020. With most early adopters being large corporations such as banks or firms with large customer service arms, the question remains as to whether chatbots are a crucial marketing tool for start-ups, or if growing businesses should invest their money elsewhere? As digital platforms continue to accelerate at an exponential rate, stakeholder engagement has transformed into a '24/7' operation, where unless a start-up has the brand strength to compete with corporate giants, not having a chatbot could be seriously detrimental to growth and sales success – here's why: Chatbots save both time and money, incorporating one into your company website will enable you to provide customers with a fast automated response, improving customer relations or driving potential sales. When developing a chatbot it's important to strike a balance between the technology's advancements and the nuances of language; in order to create an intuitive user experience Remember, the modern-day customer doesn't want to wait for an answer, just as much as a business doesn't want to miss out on a sale.
It's a catalog of reusable models that can be quickly deployed to one of the execution environments of AI Platform. The catalog has a collection of models based on popular frameworks such as Tensorflow, PyTorch, Keras, XGBoost and Scikit-learn. Each of the models is packaged in a format that can be deployed in Kubeflow, deep learning VMs backed by GPU or TPU, Jupyter Notebooks, or Google's own AI APIs. Each model is tagged with labels that make it easy to search and discover content based on a variety of attributes. AI Platform Deep Learning VM Image makes it easy and fast to instantiate a VM image containing the most popular deep learning and machine learning frameworks on a Google Compute Engine instance.
The large number of mobile devices, the volume of apps on each phone, and the basic mobility of the devices all mean there is a lot of information being creating in the mobile world. Managing that large volume of information is impossible in a reasonable timeframe using older technologies. Machine learning (ML) is critical to mobile advertising in a number of ways. Advertising is complex even in the older channels of print and broadcast. Cable increased the need for better data to more finely segment the audiences.
You might think that it would be impossible for people to value a piece of hardware over human life, yet new research from Radboud University suggests that such circumstances may exist. Bizarrely, one of these circumstances might involve a perception that robots feel pain. "It is known that military personnel may mourn a robot that is used to clear mines in the army. Funerals are organized for them. We wanted to investigate how far this empathy for robots extends, and what moral principles influence this behavior towards robots. Little research has been done in this area as of yet, " the authors explain.